Feature selection for pattern classification with Gaussian mixture models: A new objective criterion

Abstract

The selection of a feature set is an important aspect of the pattern classification process. The Fisher ratio is commonly used to rank features with respect to their effectiveness for a given classification task. The procedure used implicitly assumes a symmetric and unimodal probability density for each class. In this paper, we propose a generalized definition of the Fisher ratio as applicable to Gaussian mixture densities, which can represent multi-modal or skewed distributions. The validity and usefulness of the proposed definition is tested by a Monte Carlo simulation experiment. The correlation between the classification results and the proposed objective criterion is found to be better than that attained with the conventional uni-modal measure.